864 papers
This paper proposes a decentralized exploration framework for large robot swarms that combines a self-organizing algorithm for dynamic team formation with a novel large language model-based strategy for autonomous target selection, validated through extensive simulations.
This paper proposes and validates a data-driven control framework for magnetically actuated fish-like robots that combines a neural network-based forward dynamics model with gradient-based model predictive control and imitation learning to achieve precise path following without relying on analytical modeling.
This paper introduces a novel and efficient on-policy reinforcement learning method that trains diffusion policies by aligning policy iteration with the diffusion process, thereby overcoming computational bottlenecks in log-likelihood estimation while enabling multimodal behavior generation and entropy regularization across diverse benchmark tasks.
This paper investigates the generalization capabilities of a multimodal foundation model fine-tuned on diverse synthetic interactive data for the novel task of Open-Set Corrective Assistance, demonstrating that effective open-set assistive intelligence requires datasets encompassing multimodal grounding, defect inference, and exposure to varied scenarios.
To address the generalization challenges of vision-based imitation learning in agricultural manipulation caused by scarce data and visual domain gaps, the authors propose DRAIL, a region-aware augmentation framework that separately augments task-relevant and task-irrelevant regions to train robust policies that rely on essential features rather than spurious background cues.
This paper introduces HyperMVP, a self-supervised framework that leverages hyperbolic geometry and a novel GeoLink encoder to learn structured 3D-aware representations from a new large-scale dataset (3D-MOV), significantly outperforming Euclidean-based baselines in robotic manipulation tasks across simulation and real-world scenarios.
This paper introduces ReCouPLe, a lightweight framework that leverages natural language rationales as causal guidance to train reward models that are robust to spurious correlations and capable of zero-shot transfer to novel tasks, significantly outperforming baselines in reward accuracy and downstream policy performance under distribution shifts.
This paper introduces VPWEM, a non-Markovian visuomotor policy that combines a sliding window of recent observations with a Transformer-based episodic memory compressor to efficiently retain long-term context for robotic control, achieving significant performance improvements over state-of-the-art baselines on memory-intensive manipulation tasks while maintaining constant computational costs.
This paper proposes a viewpoint-consistent 3D adversarial texture optimization method using differentiable rendering, Expectation over Transformation with a Coarse-to-Fine curriculum, and saliency-guided perturbations to effectively expose and exploit vulnerabilities in robot visuomotor policies under dynamic camera viewpoints.
This paper introduces U-OBCA, an uncertainty-aware optimization-based collision avoidance framework that utilizes Wasserstein distributionally robust chance constraints to handle polygon-shaped robots and obstacles without geometric simplification, thereby significantly reducing conservatism and improving navigation efficiency in narrow, cluttered environments compared to existing methods.
This paper proposes a unified data-driven method for cooperative localization in heterogeneous robotic swarms that utilizes pairwise relative localization and a distributed pose-coupling strategy to achieve robust performance under weakly connected directed measurement topologies, overcoming the restrictive geometric requirements of existing approaches.
This paper addresses the challenge of person detection and tracking from an overhead crane LiDAR by curating a new annotated dataset, evaluating adapted 3D detectors like VoxelNeXt and SECOND with integrated tracking algorithms, and demonstrating high accuracy and real-time feasibility to bridge the gap between standard driving benchmarks and industrial overhead sensing.
This paper presents an augmented reality teleoperation system using Microsoft HoloLens 2 and a central computer to control the modular pneumatic soft robot PETER, achieving position estimation errors of approximately 5% of the robot's length and demonstrating the feasibility of integrating AR into soft robot control loops.
This paper proposes a Direct Contact-Tolerant (DCT) planner that leverages Vision-Language Models to generate contact-aware point clouds and solve perception-to-control optimization, enabling robots to navigate robustly and efficiently through cluttered environments with movable obstacles.
The paper proposes CoIn3D, a generalizable multi-camera 3D object detection framework that achieves strong cross-configuration transferability by explicitly addressing spatial prior discrepancies through spatial-aware feature modulation and training-free camera-aware data augmentation.
This paper introduces VinePT-Map, a resilient semantic mapping framework that leverages persistent vine trunks and support poles as structural landmarks to enable robust, season-agnostic autonomous robot localization in vineyards through a factor graph-based approach validated by multi-season field experiments.
AIM-SLAM is a dense monocular SLAM framework that leverages geometric foundation models and a novel SIGMA module for adaptive multi-view keyframe prioritization and joint Sim(3) optimization to achieve state-of-the-art performance in pose estimation and dense reconstruction.
The paper introduces GaussTwin, a real-time robotic digital twin that unifies physically grounded simulation with Gaussian Splatting-based visual correction to bridge the real-to-sim gap, thereby enhancing tracking accuracy and enabling robust closed-loop manipulation tasks.
The paper introduces SPIRIT, a perceptive shared autonomy system that dynamically switches between semi-autonomous manipulation and haptic teleoperation based on deep learning uncertainty estimates derived from Neural Tangent Kernels, thereby ensuring robust and reliable robotic performance even when perception fails.
This paper proposes a two-stage reward curriculum that decouples task-specific objectives from behavioral terms to improve exploration and training stability in robotic reinforcement learning, demonstrating superior performance and robustness across multiple environments compared to direct full-reward training.